Many centers aim to plan liver transarterial radioembolization (TARE) with dosimetry, even without CT-based attenuation correction (AC), or with unoptimized scatter correction (SC) methods. This work investigates the impact of presence vs absence of such corrections, and limited spatial resolution, on 3D dosimetry for TARE.
Three voxelized phantoms were derived from CT images of real patients with different body sizes. Simulations of 99mTc-SPECT projections were performed with the SIMIND code, assuming three activity distributions in the liver: uniform, inside a “liver's segment,” or distributing multiple uptaking nodules (“nonuniform liver”), with a tumoral liver/healthy parenchyma ratio of 5:1. Projection data were reconstructed by a commercial workstation, with OSEM protocol not specifically optimized for dosimetry (spatial resolution of 12.6 mm), with/without SC (optimized, or with parameters predefined by the manufacturer; dual energy window), and with/without AC. Activity in voxels was calculated by a relative calibration, assuming identical microspheres and 99mTc-SPECT counts spatial distribution. 3D dose distributions were calculated by convolution with 90Y voxel S-values, assuming permanent trapping of microspheres. Cumulative dose-volume histograms in lesions and healthy parenchyma from different reconstructions were compared with those obtained from the reference biodistribution (the “gold standard,” GS), assessing differences for D95%, D70%, and D50% (i.e., minimum value of the absorbed dose to a percentage of the irradiated volume). γ tool analysis with tolerance of 3%/13 mm was used to evaluate the agreement between GS and simulated cases. The influence of deep-breathing was studied, blurring the reference biodistributions with a 3D anisotropic gaussian kernel, and performing the simulations once again.
Differences of the dosimetric indicators were noticeable in some cases, always negative for lesions and distributed around zero for parenchyma. Application of AC and SC reduced systematically the differences for lesions by 5%–14% for a liver segment, and by 7%–12% for a nonuniform liver. For parenchyma, the data trend was less clear, but the overall range of variability passed from −10%/40% for a liver segment, and −10%/20% for a nonuniform liver, to −13%/6% in both cases. Applying AC, SC with preset parameters gave similar results to optimized SC, as confirmed by γ tool analysis. Moreover, γ analysis confirmed that solely AC and SC are not sufficient to obtain accurate 3D dose distribution. With breathing, the accuracy worsened severely for all dosimetric indicators, above all for lesions: with AC and optimized SC, −38%/−13% in liver's segment, −61%/−40% in the nonuniform liver. For parenchyma, D50% resulted always less sensitive to breathing and sub-optimal correction methods (difference overall range: −7%/13%).
Reconstruction protocol optimization, AC, SC, PVE and respiratory motion corrections should be implemented to obtain the best possible dosimetric accuracy. On the other side, thanks to the relative calibration, D50% inaccuracy for the healthy parenchyma from absence of AC was less than expected, while the optimization of SC was scarcely influent. The relative calibration therefore allows to perform TARE planning, basing on D50% for the healthy parenchyma, even without AC or with suboptimal corrections, rather than rely on nondosimetric methods.